{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "102ef793",
   "metadata": {},
   "source": [
    "## Breaking Down Strategy Performance to Trade Level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0e366ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfe2ecc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pyfolio as pf\n",
    "from IPython.display import Markdown, display\n",
    "from openbb import obb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "959e2485",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "obb.user.preferences.output_type = \"dataframe\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d939c0d",
   "metadata": {},
   "source": [
    "Load the mean reversion performance data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adc82049",
   "metadata": {},
   "outputs": [],
   "source": [
    "perf = pd.read_pickle(\"mean_reversion.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "724b9dae",
   "metadata": {},
   "source": [
    "Extract returns, positions, and transactions from Zipline performance DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9dc80af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e0c688",
   "metadata": {},
   "source": [
    "Rename position columns to stock symbols and cash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd1f6fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "positions.columns = [col.symbol for col in positions.columns[:-1]] + [\"cash\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b5366a8-04b5-4324-afa2-9135846b165f",
   "metadata": {},
   "source": [
    "Apply the symbol attribute to the transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "240252ec-a349-49f6-a62e-df705b5408c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions.symbol = transactions.symbol.apply(lambda s: s.symbol)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ee41642",
   "metadata": {},
   "source": [
    "Get the list of symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e845361d",
   "metadata": {},
   "outputs": [],
   "source": [
    "symbols = positions.columns[:-1].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0f8c38",
   "metadata": {},
   "source": [
    "Get screener data for the symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7da33357",
   "metadata": {},
   "outputs": [],
   "source": [
    "screener_data = obb.equity.profile(symbols, provider=\"yfinance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f42dd4",
   "metadata": {},
   "source": [
    "Create a sector map from the screener data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4963836",
   "metadata": {},
   "outputs": [],
   "source": [
    "sector_map = (\n",
    "    screener_data[[\"symbol\", \"sector\"]]\n",
    "    .set_index(\"symbol\")\n",
    "    .reindex(symbols)\n",
    "    .fillna(\"Unknown\")\n",
    "    .to_dict()[\"sector\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "defb8fe1-d686-478a-a4f8-ec7f42f6331b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AAL': 'Industrials',\n",
       " 'AAPL': 'Technology',\n",
       " 'ABBV': 'Healthcare',\n",
       " 'ADBE': 'Technology',\n",
       " 'ADP': 'Industrials',\n",
       " 'AET': 'Unknown',\n",
       " 'AGN': 'Unknown',\n",
       " 'AIG': 'Financial Services',\n",
       " 'ALXN': 'Unknown',\n",
       " 'AMAT': 'Technology',\n",
       " 'AMGN': 'Healthcare',\n",
       " 'AMZN': 'Consumer Cyclical',\n",
       " 'ANTM': 'Unknown',\n",
       " 'ARIA': 'Unknown',\n",
       " 'ATVI': 'Unknown',\n",
       " 'AVGO': 'Technology',\n",
       " 'AXP': 'Financial Services',\n",
       " 'AZO': 'Consumer Cyclical',\n",
       " 'BA': 'Industrials',\n",
       " 'BBY': 'Consumer Cyclical',\n",
       " 'BCR': 'Unknown',\n",
       " 'BIDU': 'Communication Services',\n",
       " 'BIIB': 'Healthcare',\n",
       " 'BMY': 'Healthcare',\n",
       " 'BRK_B': 'Unknown',\n",
       " 'CCE': 'Unknown',\n",
       " 'CELG': 'Unknown',\n",
       " 'CHTR': 'Communication Services',\n",
       " 'CL': 'Consumer Defensive',\n",
       " 'CMCSA': 'Communication Services',\n",
       " 'CMG': 'Consumer Cyclical',\n",
       " 'COL': 'Unknown',\n",
       " 'COST': 'Consumer Defensive',\n",
       " 'CSCO': 'Technology',\n",
       " 'CSX': 'Industrials',\n",
       " 'CTSH': 'Technology',\n",
       " 'CVS': 'Healthcare',\n",
       " 'DAL': 'Industrials',\n",
       " 'DE': 'Industrials',\n",
       " 'DG': 'Consumer Defensive',\n",
       " 'DVN': 'Energy',\n",
       " 'EA': 'Communication Services',\n",
       " 'EBAY': 'Consumer Cyclical',\n",
       " 'EFX': 'Industrials',\n",
       " 'ESRX': 'Unknown',\n",
       " 'EXPE': 'Consumer Cyclical',\n",
       " 'F': 'Consumer Cyclical',\n",
       " 'FB': 'Unknown',\n",
       " 'FDX': 'Industrials',\n",
       " 'GE': 'Industrials',\n",
       " 'GILD': 'Healthcare',\n",
       " 'GM': 'Consumer Cyclical',\n",
       " 'GMCR': 'Unknown',\n",
       " 'GOOG': 'Communication Services',\n",
       " 'GS': 'Financial Services',\n",
       " 'HAL': 'Energy',\n",
       " 'HD': 'Consumer Cyclical',\n",
       " 'HON': 'Industrials',\n",
       " 'HUM': 'Healthcare',\n",
       " 'IBM': 'Technology',\n",
       " 'INCY': 'Healthcare',\n",
       " 'INTC': 'Technology',\n",
       " 'ISRG': 'Healthcare',\n",
       " 'JNJ': 'Healthcare',\n",
       " 'KMI': 'Energy',\n",
       " 'KO': 'Consumer Defensive',\n",
       " 'KR': 'Consumer Defensive',\n",
       " 'LLY': 'Healthcare',\n",
       " 'LMT': 'Industrials',\n",
       " 'LNKD': 'Unknown',\n",
       " 'LOW': 'Consumer Cyclical',\n",
       " 'LRCX': 'Technology',\n",
       " 'M': 'Consumer Cyclical',\n",
       " 'MA': 'Financial Services',\n",
       " 'MCD': 'Consumer Cyclical',\n",
       " 'MCK': 'Healthcare',\n",
       " 'MDLZ': 'Consumer Defensive',\n",
       " 'MDT': 'Healthcare',\n",
       " 'MJN': 'Unknown',\n",
       " 'MMM': 'Industrials',\n",
       " 'MO': 'Consumer Defensive',\n",
       " 'MON': 'Unknown',\n",
       " 'MRK': 'Healthcare',\n",
       " 'MRO': 'Energy',\n",
       " 'MS': 'Financial Services',\n",
       " 'MSFT': 'Technology',\n",
       " 'MU': 'Technology',\n",
       " 'MYL': 'Unknown',\n",
       " 'NFLX': 'Communication Services',\n",
       " 'NKE': 'Consumer Cyclical',\n",
       " 'NVDA': 'Technology',\n",
       " 'ORCL': 'Technology',\n",
       " 'ORLY': 'Consumer Cyclical',\n",
       " 'OXY': 'Energy',\n",
       " 'PANW': 'Technology',\n",
       " 'PCG': 'Utilities',\n",
       " 'PCLN': 'Unknown',\n",
       " 'PEP': 'Consumer Defensive',\n",
       " 'PFE': 'Healthcare',\n",
       " 'PG': 'Consumer Defensive',\n",
       " 'PM': 'Consumer Defensive',\n",
       " 'PNRA': 'Unknown',\n",
       " 'PRGO': 'Healthcare',\n",
       " 'PXD': 'Energy',\n",
       " 'PYPL': 'Financial Services',\n",
       " 'QCOM': 'Technology',\n",
       " 'RAI': 'Unknown',\n",
       " 'REGN': 'Healthcare',\n",
       " 'SBUX': 'Consumer Cyclical',\n",
       " 'SCHW': 'Financial Services',\n",
       " 'SLB': 'Energy',\n",
       " 'SNI': 'Unknown',\n",
       " 'SRPT': 'Healthcare',\n",
       " 'STJ': 'Unknown',\n",
       " 'SYF': 'Financial Services',\n",
       " 'T': 'Communication Services',\n",
       " 'TDG': 'Industrials',\n",
       " 'TGT': 'Consumer Defensive',\n",
       " 'TJX': 'Consumer Cyclical',\n",
       " 'TSLA': 'Consumer Cyclical',\n",
       " 'TWTR': 'Unknown',\n",
       " 'TWX': 'Unknown',\n",
       " 'TXN': 'Technology',\n",
       " 'UNH': 'Healthcare',\n",
       " 'UNP': 'Industrials',\n",
       " 'UPS': 'Industrials',\n",
       " 'USB': 'Financial Services',\n",
       " 'VRX': 'Unknown',\n",
       " 'VZ': 'Communication Services',\n",
       " 'WBA': 'Healthcare',\n",
       " 'WDC': 'Technology',\n",
       " 'WFC': 'Financial Services',\n",
       " 'WFM': 'Unknown',\n",
       " 'WMT': 'Consumer Defensive'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector_map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda95884",
   "metadata": {},
   "source": [
    "Get historical price data for SPY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8926b706",
   "metadata": {},
   "outputs": [],
   "source": [
    "spy = obb.equity.price.historical(\n",
    "    \"SPY\",\n",
    "    start_date=returns.index.min(),\n",
    "    end_date=returns.index.max(),\n",
    "    provider=\"yfinance\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "378e3a89",
   "metadata": {},
   "source": [
    "Convert the index to datetime and calculate the benchmark returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc45ff79",
   "metadata": {},
   "outputs": [],
   "source": [
    "spy.index = pd.to_datetime(spy.index)\n",
    "benchmark_returns = spy.close.pct_change()\n",
    "benchmark_returns.name = \"SPY\"\n",
    "benchmark_returns = benchmark_returns.tz_localize(\"UTC\").filter(returns.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc29efc",
   "metadata": {},
   "source": [
    "Extract round trips from transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "928c0118",
   "metadata": {},
   "outputs": [],
   "source": [
    "round_trips = pf.round_trips.extract_round_trips(\n",
    "    transactions[[\"amount\", \"price\", \"symbol\"]]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c9ad4a",
   "metadata": {},
   "source": [
    "Display the round trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2d035da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pnl</th>\n",
       "      <th>open_dt</th>\n",
       "      <th>close_dt</th>\n",
       "      <th>long</th>\n",
       "      <th>rt_returns</th>\n",
       "      <th>symbol</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-29.89</td>\n",
       "      <td>2016-05-17 20:00:00+00:00</td>\n",
       "      <td>2016-05-24 20:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.015012</td>\n",
       "      <td>AAL</td>\n",
       "      <td>7 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.10</td>\n",
       "      <td>2016-07-19 20:00:00+00:00</td>\n",
       "      <td>2016-08-09 20:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.039433</td>\n",
       "      <td>AAL</td>\n",
       "      <td>21 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.12</td>\n",
       "      <td>2016-05-03 20:00:00+00:00</td>\n",
       "      <td>2016-05-24 20:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>0.028577</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>21 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-16.51</td>\n",
       "      <td>2017-06-20 20:00:00+00:00</td>\n",
       "      <td>2017-06-27 20:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.008758</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>7 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.88</td>\n",
       "      <td>2017-09-26 20:00:00+00:00</td>\n",
       "      <td>2017-10-10 20:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>0.018023</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>14 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>-2.86</td>\n",
       "      <td>2016-01-26 21:00:00+00:00</td>\n",
       "      <td>2016-02-02 21:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.044688</td>\n",
       "      <td>WMT</td>\n",
       "      <td>7 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>-54.30</td>\n",
       "      <td>2016-01-26 21:00:00+00:00</td>\n",
       "      <td>2016-02-09 21:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.028281</td>\n",
       "      <td>WMT</td>\n",
       "      <td>14 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>-40.39</td>\n",
       "      <td>2017-01-18 21:00:00+00:00</td>\n",
       "      <td>2017-01-31 21:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.019774</td>\n",
       "      <td>WMT</td>\n",
       "      <td>13 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>28.62</td>\n",
       "      <td>2017-02-28 21:00:00+00:00</td>\n",
       "      <td>2017-03-07 21:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.014944</td>\n",
       "      <td>WMT</td>\n",
       "      <td>7 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>-86.94</td>\n",
       "      <td>2017-10-31 20:00:00+00:00</td>\n",
       "      <td>2017-11-14 21:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.043294</td>\n",
       "      <td>WMT</td>\n",
       "      <td>14 days 01:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       pnl                   open_dt                  close_dt   long  \\\n",
       "0   -29.89 2016-05-17 20:00:00+00:00 2016-05-24 20:00:00+00:00   True   \n",
       "1    78.10 2016-07-19 20:00:00+00:00 2016-08-09 20:00:00+00:00  False   \n",
       "2    57.12 2016-05-03 20:00:00+00:00 2016-05-24 20:00:00+00:00   True   \n",
       "3   -16.51 2017-06-20 20:00:00+00:00 2017-06-27 20:00:00+00:00   True   \n",
       "4    35.88 2017-09-26 20:00:00+00:00 2017-10-10 20:00:00+00:00   True   \n",
       "..     ...                       ...                       ...    ...   \n",
       "254  -2.86 2016-01-26 21:00:00+00:00 2016-02-02 21:00:00+00:00  False   \n",
       "255 -54.30 2016-01-26 21:00:00+00:00 2016-02-09 21:00:00+00:00  False   \n",
       "256 -40.39 2017-01-18 21:00:00+00:00 2017-01-31 21:00:00+00:00   True   \n",
       "257  28.62 2017-02-28 21:00:00+00:00 2017-03-07 21:00:00+00:00  False   \n",
       "258 -86.94 2017-10-31 20:00:00+00:00 2017-11-14 21:00:00+00:00  False   \n",
       "\n",
       "     rt_returns symbol         duration  \n",
       "0     -0.015012    AAL  7 days 00:00:00  \n",
       "1      0.039433    AAL 21 days 00:00:00  \n",
       "2      0.028577   AAPL 21 days 00:00:00  \n",
       "3     -0.008758   AAPL  7 days 00:00:00  \n",
       "4      0.018023   AAPL 14 days 00:00:00  \n",
       "..          ...    ...              ...  \n",
       "254   -0.044688    WMT  7 days 00:00:00  \n",
       "255   -0.028281    WMT 14 days 00:00:00  \n",
       "256   -0.019774    WMT 13 days 00:00:00  \n",
       "257    0.014944    WMT  7 days 00:00:00  \n",
       "258   -0.043294    WMT 14 days 01:00:00  \n",
       "\n",
       "[259 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(round_trips)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efcc278f",
   "metadata": {},
   "source": [
    "Print round trip stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "db7bf615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Summary stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total number of round_trips</th>\n",
       "      <td>259.00</td>\n",
       "      <td>116.00</td>\n",
       "      <td>143.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent profitable</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winning round_trips</th>\n",
       "      <td>145.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Losing round_trips</th>\n",
       "      <td>114.00</td>\n",
       "      <td>63.00</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Even round_trips</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PnL stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total profit</th>\n",
       "      <td>$861.37</td>\n",
       "      <td>$-599.93</td>\n",
       "      <td>$1461.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross profit</th>\n",
       "      <td>$8204.94</td>\n",
       "      <td>$2347.53</td>\n",
       "      <td>$5857.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross loss</th>\n",
       "      <td>$-7343.57</td>\n",
       "      <td>$-2947.46</td>\n",
       "      <td>$-4396.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit factor</th>\n",
       "      <td>$1.12</td>\n",
       "      <td>$0.80</td>\n",
       "      <td>$1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. trade net profit</th>\n",
       "      <td>$3.33</td>\n",
       "      <td>$-5.17</td>\n",
       "      <td>$10.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. winning trade</th>\n",
       "      <td>$56.59</td>\n",
       "      <td>$44.29</td>\n",
       "      <td>$63.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. losing trade</th>\n",
       "      <td>$-64.42</td>\n",
       "      <td>$-46.79</td>\n",
       "      <td>$-86.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratio Avg. Win:Avg. Loss</th>\n",
       "      <td>$0.88</td>\n",
       "      <td>$0.95</td>\n",
       "      <td>$0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest winning trade</th>\n",
       "      <td>$374.66</td>\n",
       "      <td>$167.40</td>\n",
       "      <td>$374.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest losing trade</th>\n",
       "      <td>$-889.20</td>\n",
       "      <td>$-258.42</td>\n",
       "      <td>$-889.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Duration stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg duration</th>\n",
       "      <td>12 days 10:42:51.428571428</td>\n",
       "      <td>12 days 03:47:04.137931034</td>\n",
       "      <td>12 days 16:20:08.391608391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median duration</th>\n",
       "      <td>8 days 00:00:00</td>\n",
       "      <td>8 days 00:00:00</td>\n",
       "      <td>8 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longest duration</th>\n",
       "      <td>35 days 01:00:00</td>\n",
       "      <td>29 days 00:00:00</td>\n",
       "      <td>35 days 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shortest duration</th>\n",
       "      <td>6 days 00:00:00</td>\n",
       "      <td>6 days 00:00:00</td>\n",
       "      <td>6 days 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Return stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg returns all round_trips</th>\n",
       "      <td>0.26%</td>\n",
       "      <td>-0.46%</td>\n",
       "      <td>0.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns winning</th>\n",
       "      <td>3.18%</td>\n",
       "      <td>2.26%</td>\n",
       "      <td>3.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns losing</th>\n",
       "      <td>-3.46%</td>\n",
       "      <td>-2.74%</td>\n",
       "      <td>-4.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns all round_trips</th>\n",
       "      <td>0.31%</td>\n",
       "      <td>-0.21%</td>\n",
       "      <td>0.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns winning</th>\n",
       "      <td>2.03%</td>\n",
       "      <td>1.60%</td>\n",
       "      <td>2.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns losing</th>\n",
       "      <td>-2.29%</td>\n",
       "      <td>-2.28%</td>\n",
       "      <td>-2.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest winning trade</th>\n",
       "      <td>19.68%</td>\n",
       "      <td>8.31%</td>\n",
       "      <td>19.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest losing trade</th>\n",
       "      <td>-46.94%</td>\n",
       "      <td>-15.00%</td>\n",
       "      <td>-46.94%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbol stats</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AET</th>\n",
       "      <th>AGN</th>\n",
       "      <th>AIG</th>\n",
       "      <th>ALXN</th>\n",
       "      <th>AMAT</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>ANTM</th>\n",
       "      <th>ARIA</th>\n",
       "      <th>ATVI</th>\n",
       "      <th>AVGO</th>\n",
       "      <th>AXP</th>\n",
       "      <th>AZO</th>\n",
       "      <th>BA</th>\n",
       "      <th>BBY</th>\n",
       "      <th>BCR</th>\n",
       "      <th>BIDU</th>\n",
       "      <th>BIIB</th>\n",
       "      <th>BMY</th>\n",
       "      <th>BRK_B</th>\n",
       "      <th>CCE</th>\n",
       "      <th>CELG</th>\n",
       "      <th>CHTR</th>\n",
       "      <th>CL</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CMG</th>\n",
       "      <th>COL</th>\n",
       "      <th>COST</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>CSX</th>\n",
       "      <th>CTSH</th>\n",
       "      <th>CVS</th>\n",
       "      <th>DAL</th>\n",
       "      <th>DE</th>\n",
       "      <th>DG</th>\n",
       "      <th>DVN</th>\n",
       "      <th>EA</th>\n",
       "      <th>EBAY</th>\n",
       "      <th>EFX</th>\n",
       "      <th>ESRX</th>\n",
       "      <th>EXPE</th>\n",
       "      <th>F</th>\n",
       "      <th>FB</th>\n",
       "      <th>GE</th>\n",
       "      <th>GILD</th>\n",
       "      <th>GM</th>\n",
       "      <th>GMCR</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>GS</th>\n",
       "      <th>HAL</th>\n",
       "      <th>HD</th>\n",
       "      <th>HON</th>\n",
       "      <th>HUM</th>\n",
       "      <th>IBM</th>\n",
       "      <th>INCY</th>\n",
       "      <th>INTC</th>\n",
       "      <th>JNJ</th>\n",
       "      <th>KMI</th>\n",
       "      <th>KO</th>\n",
       "      <th>KR</th>\n",
       "      <th>LLY</th>\n",
       "      <th>LMT</th>\n",
       "      <th>LNKD</th>\n",
       "      <th>LOW</th>\n",
       "      <th>LRCX</th>\n",
       "      <th>M</th>\n",
       "      <th>MA</th>\n",
       "      <th>MCD</th>\n",
       "      <th>MCK</th>\n",
       "      <th>MDLZ</th>\n",
       "      <th>MDT</th>\n",
       "      <th>MJN</th>\n",
       "      <th>MMM</th>\n",
       "      <th>MO</th>\n",
       "      <th>MON</th>\n",
       "      <th>MRK</th>\n",
       "      <th>MRO</th>\n",
       "      <th>MS</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>MU</th>\n",
       "      <th>MYL</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>NKE</th>\n",
       "      <th>NVDA</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>ORLY</th>\n",
       "      <th>OXY</th>\n",
       "      <th>PANW</th>\n",
       "      <th>PCG</th>\n",
       "      <th>PCLN</th>\n",
       "      <th>PEP</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>PM</th>\n",
       "      <th>PNRA</th>\n",
       "      <th>PRGO</th>\n",
       "      <th>PXD</th>\n",
       "      <th>PYPL</th>\n",
       "      <th>QCOM</th>\n",
       "      <th>RAI</th>\n",
       "      <th>REGN</th>\n",
       "      <th>SBUX</th>\n",
       "      <th>SCHW</th>\n",
       "      <th>SLB</th>\n",
       "      <th>SNI</th>\n",
       "      <th>SRPT</th>\n",
       "      <th>STJ</th>\n",
       "      <th>SYF</th>\n",
       "      <th>T</th>\n",
       "      <th>TDG</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TJX</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TWTR</th>\n",
       "      <th>TWX</th>\n",
       "      <th>TXN</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UPS</th>\n",
       "      <th>USB</th>\n",
       "      <th>VRX</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WDC</th>\n",
       "      <th>WFC</th>\n",
       "      <th>WFM</th>\n",
       "      <th>WMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg returns all round_trips</th>\n",
       "      <td>1.22%</td>\n",
       "      <td>1.26%</td>\n",
       "      <td>-2.22%</td>\n",
       "      <td>-2.47%</td>\n",
       "      <td>4.93%</td>\n",
       "      <td>-0.32%</td>\n",
       "      <td>1.53%</td>\n",
       "      <td>1.20%</td>\n",
       "      <td>3.59%</td>\n",
       "      <td>4.21%</td>\n",
       "      <td>6.22%</td>\n",
       "      <td>-1.52%</td>\n",
       "      <td>-0.72%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>0.99%</td>\n",
       "      <td>1.74%</td>\n",
       "      <td>-4.47%</td>\n",
       "      <td>-0.93%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>4.15%</td>\n",
       "      <td>-0.22%</td>\n",
       "      <td>2.22%</td>\n",
       "      <td>-0.69%</td>\n",
       "      <td>-3.71%</td>\n",
       "      <td>-1.84%</td>\n",
       "      <td>-10.67%</td>\n",
       "      <td>-15.12%</td>\n",
       "      <td>1.19%</td>\n",
       "      <td>-0.99%</td>\n",
       "      <td>0.76%</td>\n",
       "      <td>-5.04%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>-1.81%</td>\n",
       "      <td>0.91%</td>\n",
       "      <td>-3.22%</td>\n",
       "      <td>-4.43%</td>\n",
       "      <td>1.89%</td>\n",
       "      <td>-6.93%</td>\n",
       "      <td>4.96%</td>\n",
       "      <td>-4.99%</td>\n",
       "      <td>6.85%</td>\n",
       "      <td>2.96%</td>\n",
       "      <td>-0.13%</td>\n",
       "      <td>11.92%</td>\n",
       "      <td>-2.80%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>1.77%</td>\n",
       "      <td>-0.82%</td>\n",
       "      <td>-3.98%</td>\n",
       "      <td>-1.05%</td>\n",
       "      <td>-1.30%</td>\n",
       "      <td>-0.58%</td>\n",
       "      <td>-0.45%</td>\n",
       "      <td>-2.17%</td>\n",
       "      <td>1.15%</td>\n",
       "      <td>-0.17%</td>\n",
       "      <td>3.11%</td>\n",
       "      <td>12.44%</td>\n",
       "      <td>-1.83%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>-0.66%</td>\n",
       "      <td>-1.51%</td>\n",
       "      <td>0.27%</td>\n",
       "      <td>-1.12%</td>\n",
       "      <td>2.27%</td>\n",
       "      <td>3.37%</td>\n",
       "      <td>1.34%</td>\n",
       "      <td>7.47%</td>\n",
       "      <td>0.41%</td>\n",
       "      <td>11.80%</td>\n",
       "      <td>-0.12%</td>\n",
       "      <td>2.64%</td>\n",
       "      <td>-1.41%</td>\n",
       "      <td>-1.01%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>-1.71%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.04%</td>\n",
       "      <td>0.23%</td>\n",
       "      <td>-4.11%</td>\n",
       "      <td>0.55%</td>\n",
       "      <td>8.05%</td>\n",
       "      <td>3.74%</td>\n",
       "      <td>0.14%</td>\n",
       "      <td>3.45%</td>\n",
       "      <td>1.49%</td>\n",
       "      <td>3.05%</td>\n",
       "      <td>-1.18%</td>\n",
       "      <td>1.84%</td>\n",
       "      <td>0.49%</td>\n",
       "      <td>-0.11%</td>\n",
       "      <td>5.83%</td>\n",
       "      <td>-2.70%</td>\n",
       "      <td>-0.88%</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>2.14%</td>\n",
       "      <td>-1.42%</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>0.41%</td>\n",
       "      <td>0.25%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>4.00%</td>\n",
       "      <td>-1.85%</td>\n",
       "      <td>-0.92%</td>\n",
       "      <td>2.35%</td>\n",
       "      <td>0.50%</td>\n",
       "      <td>-0.60%</td>\n",
       "      <td>4.52%</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>2.25%</td>\n",
       "      <td>7.12%</td>\n",
       "      <td>-3.66%</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>-0.32%</td>\n",
       "      <td>5.85%</td>\n",
       "      <td>-1.37%</td>\n",
       "      <td>-0.76%</td>\n",
       "      <td>-1.58%</td>\n",
       "      <td>-0.47%</td>\n",
       "      <td>-2.35%</td>\n",
       "      <td>-0.22%</td>\n",
       "      <td>2.09%</td>\n",
       "      <td>1.57%</td>\n",
       "      <td>1.33%</td>\n",
       "      <td>-10.95%</td>\n",
       "      <td>0.26%</td>\n",
       "      <td>-2.54%</td>\n",
       "      <td>0.54%</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>1.88%</td>\n",
       "      <td>-1.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns winning</th>\n",
       "      <td>3.94%</td>\n",
       "      <td>2.33%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39%</td>\n",
       "      <td>4.93%</td>\n",
       "      <td>0.42%</td>\n",
       "      <td>1.53%</td>\n",
       "      <td>2.89%</td>\n",
       "      <td>7.29%</td>\n",
       "      <td>6.42%</td>\n",
       "      <td>6.22%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.05%</td>\n",
       "      <td>1.74%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.29%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>4.15%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.22%</td>\n",
       "      <td>4.06%</td>\n",
       "      <td>0.67%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.76%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.91%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.70%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.96%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.85%</td>\n",
       "      <td>2.96%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.96%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>1.77%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.25%</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>0.22%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.99%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.22%</td>\n",
       "      <td>1.96%</td>\n",
       "      <td>3.11%</td>\n",
       "      <td>12.44%</td>\n",
       "      <td>1.17%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.21%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.27%</td>\n",
       "      <td>6.91%</td>\n",
       "      <td>1.34%</td>\n",
       "      <td>7.47%</td>\n",
       "      <td>0.41%</td>\n",
       "      <td>11.80%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.64%</td>\n",
       "      <td>0.37%</td>\n",
       "      <td>0.96%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>0.87%</td>\n",
       "      <td>1.44%</td>\n",
       "      <td>3.64%</td>\n",
       "      <td>1.87%</td>\n",
       "      <td>8.05%</td>\n",
       "      <td>3.74%</td>\n",
       "      <td>1.32%</td>\n",
       "      <td>3.45%</td>\n",
       "      <td>1.49%</td>\n",
       "      <td>3.05%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.14%</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>11.52%</td>\n",
       "      <td>5.83%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>2.14%</td>\n",
       "      <td>0.06%</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>5.18%</td>\n",
       "      <td>0.25%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>4.00%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.87%</td>\n",
       "      <td>2.35%</td>\n",
       "      <td>0.50%</td>\n",
       "      <td>0.77%</td>\n",
       "      <td>4.52%</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>2.25%</td>\n",
       "      <td>7.12%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>0.81%</td>\n",
       "      <td>5.85%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>1.38%</td>\n",
       "      <td>2.74%</td>\n",
       "      <td>2.75%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>2.09%</td>\n",
       "      <td>1.57%</td>\n",
       "      <td>1.33%</td>\n",
       "      <td>3.18%</td>\n",
       "      <td>0.99%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>1.88%</td>\n",
       "      <td>2.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns losing</th>\n",
       "      <td>-1.50%</td>\n",
       "      <td>-0.88%</td>\n",
       "      <td>-2.22%</td>\n",
       "      <td>-7.33%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.06%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.48%</td>\n",
       "      <td>-3.82%</td>\n",
       "      <td>-2.42%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.52%</td>\n",
       "      <td>-0.72%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>-1.08%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.47%</td>\n",
       "      <td>-4.16%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.22%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.81%</td>\n",
       "      <td>-8.08%</td>\n",
       "      <td>-1.84%</td>\n",
       "      <td>-10.67%</td>\n",
       "      <td>-15.12%</td>\n",
       "      <td>-1.87%</td>\n",
       "      <td>-0.99%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.04%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.81%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.22%</td>\n",
       "      <td>-4.43%</td>\n",
       "      <td>-3.53%</td>\n",
       "      <td>-6.93%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.99%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.13%</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>-2.80%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.82%</td>\n",
       "      <td>-11.21%</td>\n",
       "      <td>-4.36%</td>\n",
       "      <td>-2.81%</td>\n",
       "      <td>-0.58%</td>\n",
       "      <td>-2.90%</td>\n",
       "      <td>-2.17%</td>\n",
       "      <td>-0.92%</td>\n",
       "      <td>-2.30%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.83%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>-1.59%</td>\n",
       "      <td>-1.51%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.12%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.17%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.12%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.18%</td>\n",
       "      <td>-2.98%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.71%</td>\n",
       "      <td>-0.62%</td>\n",
       "      <td>-1.61%</td>\n",
       "      <td>-2.19%</td>\n",
       "      <td>-11.85%</td>\n",
       "      <td>-2.09%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.05%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.18%</td>\n",
       "      <td>-2.76%</td>\n",
       "      <td>-0.89%</td>\n",
       "      <td>-11.74%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.70%</td>\n",
       "      <td>-0.88%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.91%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.98%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.85%</td>\n",
       "      <td>-3.52%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.28%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.00%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.11%</td>\n",
       "      <td>-5.05%</td>\n",
       "      <td>-5.90%</td>\n",
       "      <td>-3.68%</td>\n",
       "      <td>-2.35%</td>\n",
       "      <td>-4.39%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-25.07%</td>\n",
       "      <td>-0.84%</td>\n",
       "      <td>-2.54%</td>\n",
       "      <td>-3.89%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns all round_trips</th>\n",
       "      <td>1.22%</td>\n",
       "      <td>1.80%</td>\n",
       "      <td>-2.22%</td>\n",
       "      <td>-2.47%</td>\n",
       "      <td>4.93%</td>\n",
       "      <td>-0.32%</td>\n",
       "      <td>1.53%</td>\n",
       "      <td>1.20%</td>\n",
       "      <td>4.42%</td>\n",
       "      <td>4.29%</td>\n",
       "      <td>6.22%</td>\n",
       "      <td>-1.52%</td>\n",
       "      <td>-0.72%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>0.60%</td>\n",
       "      <td>0.94%</td>\n",
       "      <td>-4.47%</td>\n",
       "      <td>-0.93%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>4.15%</td>\n",
       "      <td>-0.22%</td>\n",
       "      <td>2.22%</td>\n",
       "      <td>0.32%</td>\n",
       "      <td>-3.71%</td>\n",
       "      <td>-1.84%</td>\n",
       "      <td>-10.67%</td>\n",
       "      <td>-15.12%</td>\n",
       "      <td>0.39%</td>\n",
       "      <td>-0.99%</td>\n",
       "      <td>0.76%</td>\n",
       "      <td>-3.92%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>-1.81%</td>\n",
       "      <td>0.91%</td>\n",
       "      <td>-3.22%</td>\n",
       "      <td>-4.43%</td>\n",
       "      <td>2.63%</td>\n",
       "      <td>-6.93%</td>\n",
       "      <td>4.96%</td>\n",
       "      <td>-4.99%</td>\n",
       "      <td>6.85%</td>\n",
       "      <td>2.96%</td>\n",
       "      <td>-0.13%</td>\n",
       "      <td>14.10%</td>\n",
       "      <td>-2.80%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>1.77%</td>\n",
       "      <td>-0.81%</td>\n",
       "      <td>-3.98%</td>\n",
       "      <td>0.42%</td>\n",
       "      <td>-1.30%</td>\n",
       "      <td>-0.58%</td>\n",
       "      <td>-0.45%</td>\n",
       "      <td>-2.17%</td>\n",
       "      <td>1.15%</td>\n",
       "      <td>-0.17%</td>\n",
       "      <td>3.11%</td>\n",
       "      <td>12.44%</td>\n",
       "      <td>-1.83%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>-0.35%</td>\n",
       "      <td>-1.51%</td>\n",
       "      <td>0.27%</td>\n",
       "      <td>-1.12%</td>\n",
       "      <td>2.27%</td>\n",
       "      <td>3.37%</td>\n",
       "      <td>1.34%</td>\n",
       "      <td>7.47%</td>\n",
       "      <td>0.41%</td>\n",
       "      <td>11.80%</td>\n",
       "      <td>-0.12%</td>\n",
       "      <td>2.64%</td>\n",
       "      <td>-0.96%</td>\n",
       "      <td>-1.01%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>-1.71%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.06%</td>\n",
       "      <td>0.36%</td>\n",
       "      <td>-4.11%</td>\n",
       "      <td>1.23%</td>\n",
       "      <td>8.05%</td>\n",
       "      <td>3.74%</td>\n",
       "      <td>0.14%</td>\n",
       "      <td>1.69%</td>\n",
       "      <td>1.49%</td>\n",
       "      <td>3.05%</td>\n",
       "      <td>-0.37%</td>\n",
       "      <td>1.03%</td>\n",
       "      <td>0.33%</td>\n",
       "      <td>-0.29%</td>\n",
       "      <td>5.83%</td>\n",
       "      <td>-2.70%</td>\n",
       "      <td>-0.88%</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>2.14%</td>\n",
       "      <td>-1.42%</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>-1.10%</td>\n",
       "      <td>0.25%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>4.00%</td>\n",
       "      <td>-1.85%</td>\n",
       "      <td>-1.94%</td>\n",
       "      <td>2.35%</td>\n",
       "      <td>0.50%</td>\n",
       "      <td>-0.07%</td>\n",
       "      <td>4.52%</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>2.25%</td>\n",
       "      <td>7.12%</td>\n",
       "      <td>-3.66%</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>0.03%</td>\n",
       "      <td>5.85%</td>\n",
       "      <td>-1.39%</td>\n",
       "      <td>1.01%</td>\n",
       "      <td>-1.58%</td>\n",
       "      <td>-0.21%</td>\n",
       "      <td>-2.35%</td>\n",
       "      <td>-0.22%</td>\n",
       "      <td>2.09%</td>\n",
       "      <td>1.57%</td>\n",
       "      <td>1.33%</td>\n",
       "      <td>-1.45%</td>\n",
       "      <td>0.48%</td>\n",
       "      <td>-2.54%</td>\n",
       "      <td>1.44%</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>1.88%</td>\n",
       "      <td>-2.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns winning</th>\n",
       "      <td>3.94%</td>\n",
       "      <td>2.33%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39%</td>\n",
       "      <td>4.93%</td>\n",
       "      <td>0.42%</td>\n",
       "      <td>1.53%</td>\n",
       "      <td>2.89%</td>\n",
       "      <td>7.29%</td>\n",
       "      <td>7.80%</td>\n",
       "      <td>6.22%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.05%</td>\n",
       "      <td>0.94%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.29%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>4.15%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.22%</td>\n",
       "      <td>3.57%</td>\n",
       "      <td>0.67%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.52%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.76%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.91%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.65%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.96%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.85%</td>\n",
       "      <td>2.96%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.42%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>1.77%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.25%</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>0.22%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.99%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.22%</td>\n",
       "      <td>1.96%</td>\n",
       "      <td>3.11%</td>\n",
       "      <td>12.44%</td>\n",
       "      <td>1.17%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.21%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.27%</td>\n",
       "      <td>6.91%</td>\n",
       "      <td>1.34%</td>\n",
       "      <td>7.47%</td>\n",
       "      <td>0.41%</td>\n",
       "      <td>11.80%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.64%</td>\n",
       "      <td>0.37%</td>\n",
       "      <td>0.96%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>0.87%</td>\n",
       "      <td>1.44%</td>\n",
       "      <td>3.64%</td>\n",
       "      <td>1.87%</td>\n",
       "      <td>8.05%</td>\n",
       "      <td>3.74%</td>\n",
       "      <td>1.32%</td>\n",
       "      <td>1.69%</td>\n",
       "      <td>1.49%</td>\n",
       "      <td>3.05%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.14%</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>11.52%</td>\n",
       "      <td>5.83%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>2.14%</td>\n",
       "      <td>0.06%</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>5.18%</td>\n",
       "      <td>0.25%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>4.00%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.87%</td>\n",
       "      <td>2.35%</td>\n",
       "      <td>0.50%</td>\n",
       "      <td>0.77%</td>\n",
       "      <td>4.52%</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>2.25%</td>\n",
       "      <td>7.12%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>0.79%</td>\n",
       "      <td>5.85%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>1.38%</td>\n",
       "      <td>2.74%</td>\n",
       "      <td>2.75%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>2.09%</td>\n",
       "      <td>1.57%</td>\n",
       "      <td>1.33%</td>\n",
       "      <td>3.18%</td>\n",
       "      <td>1.10%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>1.88%</td>\n",
       "      <td>2.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns losing</th>\n",
       "      <td>-1.50%</td>\n",
       "      <td>-0.88%</td>\n",
       "      <td>-2.22%</td>\n",
       "      <td>-7.33%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.06%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.48%</td>\n",
       "      <td>-3.82%</td>\n",
       "      <td>-2.42%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.52%</td>\n",
       "      <td>-0.72%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>-1.08%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.47%</td>\n",
       "      <td>-4.16%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.22%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.81%</td>\n",
       "      <td>-8.08%</td>\n",
       "      <td>-1.84%</td>\n",
       "      <td>-10.67%</td>\n",
       "      <td>-15.12%</td>\n",
       "      <td>-1.87%</td>\n",
       "      <td>-0.99%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.92%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.81%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.22%</td>\n",
       "      <td>-4.43%</td>\n",
       "      <td>-3.53%</td>\n",
       "      <td>-6.93%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.99%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.13%</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>-2.80%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.81%</td>\n",
       "      <td>-11.21%</td>\n",
       "      <td>-4.36%</td>\n",
       "      <td>-2.81%</td>\n",
       "      <td>-0.58%</td>\n",
       "      <td>-2.90%</td>\n",
       "      <td>-2.17%</td>\n",
       "      <td>-0.92%</td>\n",
       "      <td>-2.30%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.83%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>-1.59%</td>\n",
       "      <td>-1.51%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.12%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.17%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.12%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.18%</td>\n",
       "      <td>-2.98%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.71%</td>\n",
       "      <td>-0.62%</td>\n",
       "      <td>-1.61%</td>\n",
       "      <td>-2.19%</td>\n",
       "      <td>-11.85%</td>\n",
       "      <td>-2.09%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.05%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.37%</td>\n",
       "      <td>-2.76%</td>\n",
       "      <td>-0.89%</td>\n",
       "      <td>-11.74%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.70%</td>\n",
       "      <td>-0.88%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.91%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.98%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.85%</td>\n",
       "      <td>-2.96%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.28%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.00%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.11%</td>\n",
       "      <td>-5.05%</td>\n",
       "      <td>-5.90%</td>\n",
       "      <td>-3.68%</td>\n",
       "      <td>-2.35%</td>\n",
       "      <td>-4.39%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-25.07%</td>\n",
       "      <td>-0.84%</td>\n",
       "      <td>-2.54%</td>\n",
       "      <td>-3.89%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest winning trade</th>\n",
       "      <td>3.94%</td>\n",
       "      <td>2.86%</td>\n",
       "      <td>-2.22%</td>\n",
       "      <td>2.39%</td>\n",
       "      <td>7.08%</td>\n",
       "      <td>0.42%</td>\n",
       "      <td>2.12%</td>\n",
       "      <td>2.89%</td>\n",
       "      <td>10.17%</td>\n",
       "      <td>10.68%</td>\n",
       "      <td>6.22%</td>\n",
       "      <td>-0.18%</td>\n",
       "      <td>-0.72%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>4.35%</td>\n",
       "      <td>4.01%</td>\n",
       "      <td>-4.47%</td>\n",
       "      <td>2.29%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>4.15%</td>\n",
       "      <td>-0.22%</td>\n",
       "      <td>2.22%</td>\n",
       "      <td>8.30%</td>\n",
       "      <td>0.67%</td>\n",
       "      <td>-1.84%</td>\n",
       "      <td>-10.67%</td>\n",
       "      <td>-15.12%</td>\n",
       "      <td>4.54%</td>\n",
       "      <td>-0.99%</td>\n",
       "      <td>1.37%</td>\n",
       "      <td>-0.18%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>-0.17%</td>\n",
       "      <td>0.91%</td>\n",
       "      <td>-2.81%</td>\n",
       "      <td>-2.28%</td>\n",
       "      <td>5.84%</td>\n",
       "      <td>-6.85%</td>\n",
       "      <td>4.96%</td>\n",
       "      <td>-4.99%</td>\n",
       "      <td>6.85%</td>\n",
       "      <td>2.96%</td>\n",
       "      <td>-0.13%</td>\n",
       "      <td>19.68%</td>\n",
       "      <td>-2.80%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>1.77%</td>\n",
       "      <td>-0.49%</td>\n",
       "      <td>3.25%</td>\n",
       "      <td>0.80%</td>\n",
       "      <td>0.22%</td>\n",
       "      <td>-0.58%</td>\n",
       "      <td>1.99%</td>\n",
       "      <td>-2.17%</td>\n",
       "      <td>3.22%</td>\n",
       "      <td>1.96%</td>\n",
       "      <td>3.11%</td>\n",
       "      <td>12.44%</td>\n",
       "      <td>1.17%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>1.21%</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>0.27%</td>\n",
       "      <td>-1.12%</td>\n",
       "      <td>3.80%</td>\n",
       "      <td>6.91%</td>\n",
       "      <td>1.83%</td>\n",
       "      <td>14.46%</td>\n",
       "      <td>0.41%</td>\n",
       "      <td>11.80%</td>\n",
       "      <td>-0.12%</td>\n",
       "      <td>2.64%</td>\n",
       "      <td>0.60%</td>\n",
       "      <td>0.96%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>-1.29%</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>1.68%</td>\n",
       "      <td>2.52%</td>\n",
       "      <td>3.64%</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>8.05%</td>\n",
       "      <td>6.49%</td>\n",
       "      <td>1.32%</td>\n",
       "      <td>8.31%</td>\n",
       "      <td>1.49%</td>\n",
       "      <td>4.38%</td>\n",
       "      <td>-0.03%</td>\n",
       "      <td>7.26%</td>\n",
       "      <td>2.03%</td>\n",
       "      <td>19.56%</td>\n",
       "      <td>5.83%</td>\n",
       "      <td>-2.70%</td>\n",
       "      <td>-0.88%</td>\n",
       "      <td>2.77%</td>\n",
       "      <td>3.01%</td>\n",
       "      <td>0.06%</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>5.18%</td>\n",
       "      <td>0.25%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>4.00%</td>\n",
       "      <td>-1.85%</td>\n",
       "      <td>6.87%</td>\n",
       "      <td>2.35%</td>\n",
       "      <td>0.50%</td>\n",
       "      <td>0.77%</td>\n",
       "      <td>4.52%</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>2.25%</td>\n",
       "      <td>7.12%</td>\n",
       "      <td>-3.66%</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>1.60%</td>\n",
       "      <td>5.85%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>1.74%</td>\n",
       "      <td>2.74%</td>\n",
       "      <td>4.66%</td>\n",
       "      <td>-1.25%</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>2.09%</td>\n",
       "      <td>3.12%</td>\n",
       "      <td>1.33%</td>\n",
       "      <td>6.04%</td>\n",
       "      <td>1.40%</td>\n",
       "      <td>-2.54%</td>\n",
       "      <td>4.09%</td>\n",
       "      <td>4.02%</td>\n",
       "      <td>1.88%</td>\n",
       "      <td>4.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest losing trade</th>\n",
       "      <td>-1.50%</td>\n",
       "      <td>-0.88%</td>\n",
       "      <td>-2.22%</td>\n",
       "      <td>-7.33%</td>\n",
       "      <td>2.79%</td>\n",
       "      <td>-1.06%</td>\n",
       "      <td>0.94%</td>\n",
       "      <td>-0.48%</td>\n",
       "      <td>-3.82%</td>\n",
       "      <td>-2.42%</td>\n",
       "      <td>6.22%</td>\n",
       "      <td>-2.86%</td>\n",
       "      <td>-0.72%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>-1.60%</td>\n",
       "      <td>0.28%</td>\n",
       "      <td>-4.47%</td>\n",
       "      <td>-4.16%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>4.15%</td>\n",
       "      <td>-0.22%</td>\n",
       "      <td>2.22%</td>\n",
       "      <td>-15.00%</td>\n",
       "      <td>-8.08%</td>\n",
       "      <td>-1.84%</td>\n",
       "      <td>-10.67%</td>\n",
       "      <td>-15.12%</td>\n",
       "      <td>-1.87%</td>\n",
       "      <td>-0.99%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>-11.02%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>-3.44%</td>\n",
       "      <td>0.91%</td>\n",
       "      <td>-3.64%</td>\n",
       "      <td>-6.58%</td>\n",
       "      <td>-3.53%</td>\n",
       "      <td>-7.01%</td>\n",
       "      <td>4.96%</td>\n",
       "      <td>-4.99%</td>\n",
       "      <td>6.85%</td>\n",
       "      <td>2.96%</td>\n",
       "      <td>-0.13%</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>-2.80%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>1.77%</td>\n",
       "      <td>-1.17%</td>\n",
       "      <td>-11.21%</td>\n",
       "      <td>-4.36%</td>\n",
       "      <td>-2.81%</td>\n",
       "      <td>-0.58%</td>\n",
       "      <td>-2.90%</td>\n",
       "      <td>-2.17%</td>\n",
       "      <td>-0.92%</td>\n",
       "      <td>-2.30%</td>\n",
       "      <td>3.11%</td>\n",
       "      <td>12.44%</td>\n",
       "      <td>-4.83%</td>\n",
       "      <td>-0.55%</td>\n",
       "      <td>-2.84%</td>\n",
       "      <td>-2.82%</td>\n",
       "      <td>0.27%</td>\n",
       "      <td>-1.12%</td>\n",
       "      <td>0.73%</td>\n",
       "      <td>-0.17%</td>\n",
       "      <td>0.86%</td>\n",
       "      <td>0.49%</td>\n",
       "      <td>0.41%</td>\n",
       "      <td>11.80%</td>\n",
       "      <td>-0.12%</td>\n",
       "      <td>2.64%</td>\n",
       "      <td>-4.29%</td>\n",
       "      <td>-2.98%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>-2.13%</td>\n",
       "      <td>-0.62%</td>\n",
       "      <td>-1.61%</td>\n",
       "      <td>-2.19%</td>\n",
       "      <td>-11.85%</td>\n",
       "      <td>-2.09%</td>\n",
       "      <td>8.05%</td>\n",
       "      <td>0.99%</td>\n",
       "      <td>-1.05%</td>\n",
       "      <td>0.35%</td>\n",
       "      <td>1.49%</td>\n",
       "      <td>1.72%</td>\n",
       "      <td>-3.96%</td>\n",
       "      <td>-2.76%</td>\n",
       "      <td>-0.89%</td>\n",
       "      <td>-19.43%</td>\n",
       "      <td>5.83%</td>\n",
       "      <td>-2.70%</td>\n",
       "      <td>-0.88%</td>\n",
       "      <td>2.74%</td>\n",
       "      <td>1.27%</td>\n",
       "      <td>-2.91%</td>\n",
       "      <td>2.76%</td>\n",
       "      <td>-2.85%</td>\n",
       "      <td>0.25%</td>\n",
       "      <td>0.82%</td>\n",
       "      <td>4.00%</td>\n",
       "      <td>-1.85%</td>\n",
       "      <td>-6.68%</td>\n",
       "      <td>2.35%</td>\n",
       "      <td>0.50%</td>\n",
       "      <td>-2.50%</td>\n",
       "      <td>4.52%</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>2.25%</td>\n",
       "      <td>7.12%</td>\n",
       "      <td>-3.66%</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>-2.27%</td>\n",
       "      <td>5.85%</td>\n",
       "      <td>-2.84%</td>\n",
       "      <td>-5.05%</td>\n",
       "      <td>-5.90%</td>\n",
       "      <td>-6.10%</td>\n",
       "      <td>-3.45%</td>\n",
       "      <td>-4.39%</td>\n",
       "      <td>2.09%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>1.33%</td>\n",
       "      <td>-46.94%</td>\n",
       "      <td>-0.85%</td>\n",
       "      <td>-2.54%</td>\n",
       "      <td>-3.89%</td>\n",
       "      <td>3.89%</td>\n",
       "      <td>1.88%</td>\n",
       "      <td>-4.47%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf.round_trips.print_round_trip_stats(\n",
    "    round_trips.rename(columns={\"rt_returns\": \"returns\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9675eead",
   "metadata": {},
   "source": [
    "Plot round trip lifetimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "04420d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf.plotting.plot_round_trip_lifetimes(round_trips)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28171d2b",
   "metadata": {},
   "source": [
    "Apply sector mappings to round trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "919ded5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "round_trips_by_sector = pf.round_trips.apply_sector_mappings_to_round_trips(\n",
    "    round_trips, sector_map\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e81b77f",
   "metadata": {},
   "source": [
    "Display the round trips by sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af36d0f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pnl</th>\n",
       "      <th>open_dt</th>\n",
       "      <th>close_dt</th>\n",
       "      <th>long</th>\n",
       "      <th>rt_returns</th>\n",
       "      <th>symbol</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-29.89</td>\n",
       "      <td>2016-05-17 20:00:00+00:00</td>\n",
       "      <td>2016-05-24 20:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.015012</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>7 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.10</td>\n",
       "      <td>2016-07-19 20:00:00+00:00</td>\n",
       "      <td>2016-08-09 20:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.039433</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>21 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.12</td>\n",
       "      <td>2016-05-03 20:00:00+00:00</td>\n",
       "      <td>2016-05-24 20:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>0.028577</td>\n",
       "      <td>Technology</td>\n",
       "      <td>21 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-16.51</td>\n",
       "      <td>2017-06-20 20:00:00+00:00</td>\n",
       "      <td>2017-06-27 20:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.008758</td>\n",
       "      <td>Technology</td>\n",
       "      <td>7 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.88</td>\n",
       "      <td>2017-09-26 20:00:00+00:00</td>\n",
       "      <td>2017-10-10 20:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>0.018023</td>\n",
       "      <td>Technology</td>\n",
       "      <td>14 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>-2.86</td>\n",
       "      <td>2016-01-26 21:00:00+00:00</td>\n",
       "      <td>2016-02-02 21:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.044688</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>7 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>-54.30</td>\n",
       "      <td>2016-01-26 21:00:00+00:00</td>\n",
       "      <td>2016-02-09 21:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.028281</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>14 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>-40.39</td>\n",
       "      <td>2017-01-18 21:00:00+00:00</td>\n",
       "      <td>2017-01-31 21:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.019774</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>13 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>28.62</td>\n",
       "      <td>2017-02-28 21:00:00+00:00</td>\n",
       "      <td>2017-03-07 21:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.014944</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>7 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>-86.94</td>\n",
       "      <td>2017-10-31 20:00:00+00:00</td>\n",
       "      <td>2017-11-14 21:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.043294</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>14 days 01:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       pnl                   open_dt                  close_dt   long  \\\n",
       "0   -29.89 2016-05-17 20:00:00+00:00 2016-05-24 20:00:00+00:00   True   \n",
       "1    78.10 2016-07-19 20:00:00+00:00 2016-08-09 20:00:00+00:00  False   \n",
       "2    57.12 2016-05-03 20:00:00+00:00 2016-05-24 20:00:00+00:00   True   \n",
       "3   -16.51 2017-06-20 20:00:00+00:00 2017-06-27 20:00:00+00:00   True   \n",
       "4    35.88 2017-09-26 20:00:00+00:00 2017-10-10 20:00:00+00:00   True   \n",
       "..     ...                       ...                       ...    ...   \n",
       "254  -2.86 2016-01-26 21:00:00+00:00 2016-02-02 21:00:00+00:00  False   \n",
       "255 -54.30 2016-01-26 21:00:00+00:00 2016-02-09 21:00:00+00:00  False   \n",
       "256 -40.39 2017-01-18 21:00:00+00:00 2017-01-31 21:00:00+00:00   True   \n",
       "257  28.62 2017-02-28 21:00:00+00:00 2017-03-07 21:00:00+00:00  False   \n",
       "258 -86.94 2017-10-31 20:00:00+00:00 2017-11-14 21:00:00+00:00  False   \n",
       "\n",
       "     rt_returns              symbol         duration  \n",
       "0     -0.015012         Industrials  7 days 00:00:00  \n",
       "1      0.039433         Industrials 21 days 00:00:00  \n",
       "2      0.028577          Technology 21 days 00:00:00  \n",
       "3     -0.008758          Technology  7 days 00:00:00  \n",
       "4      0.018023          Technology 14 days 00:00:00  \n",
       "..          ...                 ...              ...  \n",
       "254   -0.044688  Consumer Defensive  7 days 00:00:00  \n",
       "255   -0.028281  Consumer Defensive 14 days 00:00:00  \n",
       "256   -0.019774  Consumer Defensive 13 days 00:00:00  \n",
       "257    0.014944  Consumer Defensive  7 days 00:00:00  \n",
       "258   -0.043294  Consumer Defensive 14 days 01:00:00  \n",
       "\n",
       "[259 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(round_trips_by_sector)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93102a28",
   "metadata": {},
   "source": [
    "Plot round trip lifetimes by sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f2cbb16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf.plotting.plot_round_trip_lifetimes(\n",
    "    round_trips_by_sector.rename(columns={\"rt_returns\": \"returns\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb56f35",
   "metadata": {},
   "source": [
    "**Jason Strimpel** is the founder of <a href='https://pyquantnews.com/'>PyQuant News</a> and co-founder of <a href='https://www.tradeblotter.io/'>Trade Blotter</a>. His career in algorithmic trading spans 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in Finance and Economics and a Master's in Quantitative Finance from the Illinois Institute of Technology. His career spans America, Europe, and Asia. He shares his expertise through the <a href='https://pyquantnews.com/subscribe-to-the-pyquant-newsletter/'>PyQuant Newsletter</a>, social media, and has taught over 1,000+ algorithmic trading with Python in his popular course **<a href='https://gettingstartedwithpythonforquantfinance.com/'>Getting Started With Python for Quant Finance</a>**. All code is for educational purposes only. Nothing provided here is financial advise. Use at your own risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3d19873-45d2-4372-ad87-1c4ef4de90b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
